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<!-- ==================== MODULE DESCRIPTION ==================== -->
<h1 class="epydoc">Module Plotting</h1><p class="nomargin-top"><span class="codelink"><a href="dadi.Plotting-pysrc.html">source&nbsp;code</a></span></p>
<pre class="literalblock">

Routines for Plotting comparisons between model and data.

These can serve as inspiration for custom routines for one's own purposes.
Note that all the plotting is done with pylab. To see additional pylab methods:
&quot;import pylab; help(pylab)&quot;. Pylab's many functions are documented at 
http://matplotlib.sourceforge.net/contents.html

</pre>

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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_1d_fs" class="summary-sig-name">plot_1d_fs</a>(<span class="summary-sig-arg">fs</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>)</span><br />
      Plot a 1-dimensional frequency spectrum.</td>
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            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_fs">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_1d_comp_multinom" class="summary-sig-name">plot_1d_comp_multinom</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">plot_masked</span>=<span class="summary-sig-default">False</span>)</span><br />
      Mulitnomial comparison between 1d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_comp_multinom">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_1d_comp_Poisson" class="summary-sig-name">plot_1d_comp_Poisson</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">plot_masked</span>=<span class="summary-sig-default">False</span>)</span><br />
      Poisson comparison between 1d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_comp_Poisson">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_single_2d_sfs" class="summary-sig-name">plot_single_2d_sfs</a>(<span class="summary-sig-arg">sfs</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">ax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">extend</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">neither</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">colorbar</span>=<span class="summary-sig-default">True</span>)</span><br />
      Heatmap of single 2d SFS.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_single_2d_sfs">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_2d_resid" class="summary-sig-name">plot_2d_resid</a>(<span class="summary-sig-arg">resid</span>,
        <span class="summary-sig-arg">resid_range</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">ax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">extend</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">neither</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">colorbar</span>=<span class="summary-sig-default">True</span>)</span><br />
      Linear heatmap of 2d residual array.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_resid">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_2d_comp_multinom" class="summary-sig-name">plot_2d_comp_multinom</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">resid_range</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">adjust</span>=<span class="summary-sig-default">True</span>)</span><br />
      Mulitnomial comparison between 2d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_comp_multinom">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_2d_comp_Poisson" class="summary-sig-name">plot_2d_comp_Poisson</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">resid_range</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">adjust</span>=<span class="summary-sig-default">True</span>)</span><br />
      Poisson comparison between 2d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_comp_Poisson">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_3d_comp_multinom" class="summary-sig-name">plot_3d_comp_multinom</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">resid_range</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">adjust</span>=<span class="summary-sig-default">True</span>)</span><br />
      Multinomial comparison between 3d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_comp_multinom">source&nbsp;code</a></span>
            
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      <span class="summary-type">&nbsp;</span>
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_3d_comp_Poisson" class="summary-sig-name">plot_3d_comp_Poisson</a>(<span class="summary-sig-arg">model</span>,
        <span class="summary-sig-arg">data</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">resid_range</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">fig_num</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">residual</span>=<span class="summary-sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="summary-sig-arg">adjust</span>=<span class="summary-sig-default">True</span>)</span><br />
      Poisson comparison between 3d model and data.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_comp_Poisson">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_3d_spectrum" class="summary-sig-name">plot_3d_spectrum</a>(<span class="summary-sig-arg">fs</span>,
        <span class="summary-sig-arg">fignum</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>)</span><br />
      Logarithmic heatmap of single 3d FS.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_spectrum">source&nbsp;code</a></span>
            
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          <td><span class="summary-sig"><a href="dadi.Plotting-module.html#plot_3d_spectrum_mayavi" class="summary-sig-name">plot_3d_spectrum_mayavi</a>(<span class="summary-sig-arg">fs</span>,
        <span class="summary-sig-arg">fignum</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmin</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">vmax</span>=<span class="summary-sig-default">None</span>,
        <span class="summary-sig-arg">pop_ids</span>=<span class="summary-sig-default">None</span>)</span><br />
      Logarithmic heatmap of single 3d FS.</td>
          <td align="right" valign="top">
            <span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_spectrum_mayavi">source&nbsp;code</a></span>
            
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        <a href="dadi.Plotting-module.html#_ctf" class="summary-name" onclick="show_private();">_ctf</a> = <code title="matplotlib.ticker.FuncFormatter(lambda x, pos: '%i' %(x-0.4))">matplotlib.ticker.FuncFormatter(lambda x, pos: '%i' %(x<code class="variable-ellipsis">...</code></code>
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 (False, True): 'min',
 (True, False): 'max',
 (True, True): 'neither'}"><code class="variable-group">{</code><code class="variable-group">(</code>False<code class="variable-op">, </code>False<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">both</code><code class="variable-quote">'</code><code class="variable-op">, </code><code class="variable-group">(</code>False<code class="variable-op">, </code>True<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">min</code><code class="variable-ellipsis">...</code></code>
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        <a name="__package__"></a><span class="summary-name">__package__</span> = <code title="'dadi'"><code class="variable-quote">'</code><code class="variable-string">dadi</code><code class="variable-quote">'</code></code>
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  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_1d_fs</span>(<span class="sig-arg">fs</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_fs">source&nbsp;code</a></span>&nbsp;
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  <pre class="literalblock">

Plot a 1-dimensional frequency spectrum.

fs: 1-dimensional Spectrum
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.

Note that all the plotting is done with pylab. To see additional pylab
methods: &quot;import pylab; help(pylab)&quot;. Pylab's many functions are documented
at http://matplotlib.sourceforge.net/contents.html

</pre>
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  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_1d_comp_multinom</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">plot_masked</span>=<span class="sig-default">False</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_comp_multinom">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Mulitnomial comparison between 1d model and data.


model: 1-dimensional model SFS
data: 1-dimensional data SFS
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
plot_masked: Additionally plots (in open circles) results for points in the 
             model or data that were masked.

This comparison is multinomial in that it rescales the model to optimally
fit the data.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_1d_comp_Poisson"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_1d_comp_Poisson</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">plot_masked</span>=<span class="sig-default">False</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_1d_comp_Poisson">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Poisson comparison between 1d model and data.


model: 1-dimensional model SFS
data: 1-dimensional data SFS
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
plot_masked: Additionally plots (in open circles) results for points in the 
             model or data that were masked.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_single_2d_sfs"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_single_2d_sfs</span>(<span class="sig-arg">sfs</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">ax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">extend</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">neither</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">colorbar</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_single_2d_sfs">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Heatmap of single 2d SFS. 

If vmax is greater than a factor of 10, plot on log scale.

sfs: SFS to plot
vmin: Values in sfs below vmin are masked in plot.
vmax: Values in sfs above vmax saturate the color spectrum.
ax: Axes object to plot into. If None, the result of pylab.gca() is used.
pop_ids: If not None, override pop_ids stored in Spectrum.
extend: Whether the colorbar should have 'extension' arrows. See
        help(pylab.colorbar) for more details.
colorbar: Should we plot a colorbar?

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_2d_resid"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_2d_resid</span>(<span class="sig-arg">resid</span>,
        <span class="sig-arg">resid_range</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">ax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">extend</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">neither</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">colorbar</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_resid">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Linear heatmap of 2d residual array.

sfs: Residual array to plot.
resid_range: Values &gt; resid range or &lt; resid_range saturate the color
             spectrum.
ax: Axes object to plot into. If None, the result of pylab.gca() is used.
pop_ids: If not None, override pop_ids stored in Spectrum.
extend: Whether the colorbar should have 'extension' arrows. See
        help(pylab.colorbar) for more details.
colorbar: Should we plot a colorbar?

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_2d_comp_multinom"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_2d_comp_multinom</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">resid_range</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">adjust</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_comp_multinom">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Mulitnomial comparison between 2d model and data.


model: 2-dimensional model SFS
data: 2-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
            vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
        manipulation of plots, it may be useful to make this False.

This comparison is multinomial in that it rescales the model to optimally
fit the data.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_2d_comp_Poisson"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_2d_comp_Poisson</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">resid_range</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">adjust</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_2d_comp_Poisson">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Poisson comparison between 2d model and data.


model: 2-dimensional model SFS
data: 2-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
            vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
        manipulation of plots, it may be useful to make this False.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_3d_comp_multinom"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_3d_comp_multinom</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">resid_range</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">adjust</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_comp_multinom">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Multinomial comparison between 3d model and data.


model: 3-dimensional model SFS
data: 3-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
            vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
        manipulation of plots, it may be useful to make this False.

This comparison is multinomial in that it rescales the model to optimally
fit the data.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_3d_comp_Poisson"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_3d_comp_Poisson</span>(<span class="sig-arg">model</span>,
        <span class="sig-arg">data</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">resid_range</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">fig_num</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">residual</span>=<span class="sig-default"><code class="variable-quote">'</code><code class="variable-string">Anscombe</code><code class="variable-quote">'</code></span>,
        <span class="sig-arg">adjust</span>=<span class="sig-default">True</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_comp_Poisson">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Poisson comparison between 3d model and data.


model: 3-dimensional model SFS
data: 3-dimensional data SFS
vmin, vmax: Minimum and maximum values plotted for sfs are vmin and
            vmax respectively.
resid_range: Residual plot saturates at +- resid_range.
fig_num: Clear and use figure fig_num for display. If None, an new figure
         window is created.
pop_ids: If not None, override pop_ids stored in Spectrum.
residual: 'Anscombe' for Anscombe residuals, which are more normally
          distributed for Poisson sampling. 'linear' for the linear
          residuals, which can be less biased.
adjust: Should method use automatic 'subplots_adjust'? For advanced
        manipulation of plots, it may be useful to make this False.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_3d_spectrum"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_3d_spectrum</span>(<span class="sig-arg">fs</span>,
        <span class="sig-arg">fignum</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_spectrum">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Logarithmic heatmap of single 3d FS.

Note that this method is slow, because it relies on matplotlib's software
rendering. For faster and better looking plots, use plot_3d_spectrum_mayavi.

fs: FS to plot
vmin: Values in fs below vmin are masked in plot.
vmax: Values in fs above vmax saturate the color spectrum.
fignum: Figure number to plot into. If None, a new figure will be created.
pop_ids: If not None, override pop_ids stored in Spectrum.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<a name="plot_3d_spectrum_mayavi"></a>
<div>
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <table width="100%" cellpadding="0" cellspacing="0" border="0">
  <tr valign="top"><td>
  <h3 class="epydoc"><span class="sig"><span class="sig-name">plot_3d_spectrum_mayavi</span>(<span class="sig-arg">fs</span>,
        <span class="sig-arg">fignum</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmin</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">vmax</span>=<span class="sig-default">None</span>,
        <span class="sig-arg">pop_ids</span>=<span class="sig-default">None</span>)</span>
  </h3>
  </td><td align="right" valign="top"
    ><span class="codelink"><a href="dadi.Plotting-pysrc.html#plot_3d_spectrum_mayavi">source&nbsp;code</a></span>&nbsp;
    </td>
  </tr></table>
  
  <pre class="literalblock">

Logarithmic heatmap of single 3d FS.

This method relies on MayaVi2's mlab interface. See http://code.enthought.com/projects/mayavi/docs/development/html/mayavi/mlab.html . To edit plot
properties, click leftmost icon in the toolbar.

If you get an ImportError upon calling this function, it is likely that you
don't have mayavi installed.

fs: FS to plot
vmin: Values in fs below vmin are masked in plot.
vmax: Values in fs above vmax saturate the color spectrum.
fignum: Figure number to plot into. If None, a new figure will be created.
        Note that these are MayaVi figures, which are separate from
        matplotlib figures.
pop_ids: If not None, override pop_ids stored in Spectrum.

</pre>
  <dl class="fields">
  </dl>
</td></tr></table>
</div>
<br />
<!-- ==================== VARIABLES DETAILS ==================== -->
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<tr bgcolor="#70b0f0" class="table-header">
  <td colspan="2" class="table-header">
    <table border="0" cellpadding="0" cellspacing="0" width="100%">
      <tr valign="top">
        <td align="left"><span class="table-header">Variables Details</span></td>
        <td align="right" valign="top"
         ><span class="options">[<a href="#section-VariablesDetails"
         class="privatelink" onclick="toggle_private();"
         >hide private</a>]</span></td>
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<table class="details" border="1" cellpadding="3"
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<tr><td>
  <h3 class="epydoc">_ctf</h3>
  
  <dl class="fields">
  </dl>
  <dl class="fields">
    <dt>Value:</dt>
      <dd><table><tr><td><pre class="variable">
matplotlib.ticker.FuncFormatter(lambda x, pos: '%i' %(x-0.4))
</pre></td></tr></table>
</dd>
  </dl>
</td></tr></table>
</div>
<a name="_extend_mapping"></a>
<div class="private">
<table class="details" border="1" cellpadding="3"
       cellspacing="0" width="100%" bgcolor="white">
<tr><td>
  <h3 class="epydoc">_extend_mapping</h3>
  
  <dl class="fields">
  </dl>
  <dl class="fields">
    <dt>Value:</dt>
      <dd><table><tr><td><pre class="variable">
<code class="variable-group">{</code><code class="variable-group">(</code>False<code class="variable-op">, </code>False<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">both</code><code class="variable-quote">'</code><code class="variable-op">,</code>
 <code class="variable-group">(</code>False<code class="variable-op">, </code>True<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">min</code><code class="variable-quote">'</code><code class="variable-op">,</code>
 <code class="variable-group">(</code>True<code class="variable-op">, </code>False<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">max</code><code class="variable-quote">'</code><code class="variable-op">,</code>
 <code class="variable-group">(</code>True<code class="variable-op">, </code>True<code class="variable-group">)</code><code class="variable-op">: </code><code class="variable-quote">'</code><code class="variable-string">neither</code><code class="variable-quote">'</code><code class="variable-group">}</code>
</pre></td></tr></table>
</dd>
  </dl>
</td></tr></table>
</div>
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